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RETRACTED ARTICLE: An improved secure file deduplication avoidance using CKHO based deep learning model in a cloud environment

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This article was retracted on 15 July 2024

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Abstract

Data deduplication is a process that gets rid of excessive duplicates of data and minimizes the storage capacity to a large extent. This process mainly optimizes redundancies without compromising the data fidelity or integrity. However, the major challenge faced by most data deduplication systems is secure cloud storage. Cloud computing relies on the ability and security of all information. In the case of distributed storage, data protection and security are critical. This paper presents a Secure Cloud Framework for owners to effectively handle cloud-based information and provide high security for information (SCF). Weaknesses, Cross-Site Scripting (XSS), SQL perfusion, adverse processing, and wrapping are all examples of significant attacks in the cloud. This paper proposes an improved Secure File Deduplication Avoidance (SFDA) algorithm for block-level deduplication and security. The deduplication process allows cloud customers to adequately manage the distributed storage space by avoiding redundant information and saving transfer speed. A deep learning classifier is used to distinguish the familiar and unfamiliar data. A dynamic perfect hashing scheme is used in the SFDA approach to perform convergent encryption and offer secure storage. The Chaotic krill herd optimization (CKHO) algorithm is used for the optimal secret key generation process of the Advanced Encryption Standard (AES) algorithm. In this way, the unfamiliar data are encrypted one more time and stored in the cloud. The efficiency of the results is demonstrated via the experiments conducted in terms of computational cost, communication overhead, deduplication rate, and attack level. For file sizes of 8 MB, 16 MB, 32 MB, and 64 MB, the proposed methodology yields a deduplication rate of 53%, 62%, 54%, and 44%, respectively. The dynamic perfect hashing and the optimal key generation using the CKHO algorithm minimizes the data update time and the time taken to update a total of 1024 MB data is 341.5 ms. The improved SFDA algorithm's optimal key selection approach reduces the impact of an attack by up to 12% for a data size of 50 MB, whereas the existing system is mostly impacted by data size, and its attack level rises by up to 19 percent for the same data size.

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  1. Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai-600119, India

    N. Mageshkumar & L. Lakshmanan

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  1. N. Mageshkumar
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Both the authors are equally contributed their skills and effort to produce this article.

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Correspondence to N. Mageshkumar.

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This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s11227-024-06360-x"

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Mageshkumar, N., Lakshmanan, L. RETRACTED ARTICLE: An improved secure file deduplication avoidance using CKHO based deep learning model in a cloud environment. J Supercomput 78, 14892–14918 (2022). https://doi.org/10.1007/s11227-022-04436-0

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